PeakPerformance - A tool for Bayesian inference-based fitting of LC-MS/MS peaks
PeakPerformance - A tool for Bayesian inference-based fitting of LC-MS/MS peaks - Published in JOSS (2024)
Science Score: 96.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 7 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
○Academic email domains
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✓Institutional organization owner
Organization jubiotech has institutional domain (www.fz-juelich.de) -
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
A Python toolbox for Bayesian inference of peak areas.
Basic Info
- Host: GitHub
- Owner: JuBiotech
- License: agpl-3.0
- Language: Python
- Default Branch: main
- Homepage: https://peak-performance.readthedocs.io/en/latest
- Size: 578 MB
Statistics
- Stars: 6
- Watchers: 4
- Forks: 0
- Open Issues: 2
- Releases: 5
Topics
Metadata Files
README.md
About PeakPerformance
PeakPerformance employs Bayesian modeling for chromatographic peak data fitting. This has the innate advantage of providing uncertainty quantification while jointly estimating all peak parameters united in a single peak model. As Markov Chain Monte Carlo (MCMC) methods are utilized to infer the posterior probability distribution, convergence checks and the aformentioned uncertainty quantification are applied as novel quality metrics for a robust peak recognition.
Installation
It is highly recommended to follow the following steps and install PeakPerformance in a fresh Python environment:
1. Install the package manager Mamba.
Choose the latest installer at the top of the page, click on "show all assets", and download an installer denominated by "Mambaforge-version number-name of your OS.exe", so e.g. "Mambaforge-23.3.1-1-Windows-x86_64.exe" for a Windows 64 bit operating system. Then, execute the installer to install mamba and activate the option "Add Mambaforge to my PATH environment variable".
If you have already installed Miniconda, you can install Mamba on top of it but there are compatibility issues with Anaconda.
The newest conda version should also work, just replace mamba with conda in step 2.
- Create a new Python environment in the command line using the provided
environment.ymlfile from the repo. Downloadenvironment.ymlfirst, then navigate to its location on the command line interface and run the following command:mamba env create -f environment.yml
Naturally, it is alternatively possible to just install PeakPerformance via pip:
bash
pip install peak-performance
First steps
Be sure to check out our thorough documentation. It contains not only information on how to install PeakPerformance and prepare raw data for its application but also detailed treatises about the implemented model structures, validation with both synthetic and experimental data against a commercially available vendor software, exemplary usage of diagnostic plots and investigation of various effects. Furthermore, you will find example notebooks and data sets showcasing different aspects of PeakPerformance.
How to contribute
If you encounter bugs while using PeakPerformance, please bring them to our attention by opening an issue. When doing so, describe the problem in detail and add screenshots/code snippets and whatever other helpful material you can provide. When contributing code, create a local clone of PeakPerformance, create a new branch, and open a pull request (PR).
How to cite
Head over to Zenodo to generate a BibTeX citation for the latest release. In addition to the utilized software version, please cite our scientific publication over at the Journal of Open Source Software (JOSS). A detailed citation can be found in CITATION.cff and in the sidebar.
Owner
- Name: IBG-1: Biotechnology
- Login: JuBiotech
- Kind: organization
- Location: Forschungszentrum Jülich
- Website: https://www.fz-juelich.de/ibg/ibg-1/EN/Home/home_node.html
- Repositories: 8
- Profile: https://github.com/JuBiotech
JOSS Publication
PeakPerformance - A tool for Bayesian inference-based fitting of LC-MS/MS peaks
Authors
Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany, RWTH Aachen University, Aachen, Germany
Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany
Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany, Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany
Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany, Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany
Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, Jülich, Germany
Editor
Charlotte Soneson
Tags
Peak fitting Bayesian inference Chromatography LC-MS/MS Uncertainty quantificationGitHub Events
Total
- Create event: 15
- Release event: 1
- Issues event: 13
- Watch event: 4
- Delete event: 13
- Issue comment event: 22
- Push event: 34
- Pull request review comment event: 4
- Pull request review event: 10
- Pull request event: 30
- Fork event: 2
Last Year
- Create event: 15
- Release event: 1
- Issues event: 13
- Watch event: 4
- Delete event: 13
- Issue comment event: 22
- Push event: 34
- Pull request review comment event: 4
- Pull request review event: 10
- Pull request event: 30
- Fork event: 2
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 6
- Total pull requests: 43
- Average time to close issues: 6 days
- Average time to close pull requests: 16 days
- Total issue authors: 3
- Total pull request authors: 5
- Average comments per issue: 2.0
- Average comments per pull request: 0.98
- Merged pull requests: 39
- Bot issues: 0
- Bot pull requests: 12
Past Year
- Issues: 5
- Pull requests: 28
- Average time to close issues: 6 days
- Average time to close pull requests: 9 days
- Issue authors: 2
- Pull request authors: 5
- Average comments per issue: 2.4
- Average comments per pull request: 0.89
- Merged pull requests: 26
- Bot issues: 0
- Bot pull requests: 5
Top Authors
Issue Authors
- Adafede (4)
- michaelosthege (2)
- dependabot[bot] (1)
- lazear (1)
Pull Request Authors
- Y0dler (30)
- michaelosthege (23)
- dependabot[bot] (21)
- lazear (2)
- xuanxu (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 74 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
- Total maintainers: 1
pypi.org: peak-performance
A Python toolbox to fit chromatography peaks with uncertainty.
- Documentation: https://peak-performance.readthedocs.io/
- License: AGPLv3
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Latest release: 0.7.1
published over 1 year ago
Rankings
Maintainers (1)
Dependencies
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